class_docstr: FPN_CARAFE is a more flexible implementation of FPN. It allows more
  choice for upsample methods during the top-down pathway.
  
  It can reproduce the preformance of ICCV 2019 paper
  CARAFE: Content-Aware ReAssembly of FEatures
  Please refer to https://arxiv.org/abs/1905.02188 for more details.
  
  Args:
      in_channels (list[int]): Number of channels for each input feature map.
      out_channels (int): Output channels of feature pyramids.
      num_outs (int): Number of output stages.
      start_level (int): Start level of feature pyramids.
          (Default: 0)
      end_level (int): End level of feature pyramids.
          (Default: -1 indicates the last level).
      norm_cfg (dict): Dictionary to construct and config norm layer.
      activate (str): Type of activation function in ConvModule
          (Default: None indicates w/o activation).
      order (dict): Order of components in ConvModule.
      upsample (str): Type of upsample layer.
      upsample_cfg (dict): Dictionary to construct and config upsample layer.
docstr: Add tensors ``a`` and ``b`` that might have different sizes.
docstr_tok: ["Add","tensors","``","a","``","and","``","b","``","that","might","have","different","sizes","."]
code: def tensor_add(self, a, b):
      """Add tensors ``a`` and ``b`` that might have different sizes."""
      if a.size() == b.size():
          c = a + b
      else:
          c = a + self.slice_as(b, a)
      return c
  
file_path: insightface/detection/scrfd/mmdet/models/necks/fpn_carafe.py
imports: import torch.nn
  from mmcv.cnn import ConvModule, build_upsample_layer, xavier_init
  from mmcv.ops.carafe import CARAFEPack
  from ..builder import NECKS
label_desc: Use image node_id 4 for calling the FPN_CARAFE obj's underlying member method code with example usage: obj.tensor_add(a, b) and returns: c